IEEE Trans Cybern. 2023 Apr;53(4):2685-2697. doi: 10.1109/TCYB.2022.3175533. Epub 2023 Mar 16.
The radial basis function (RBF) model and the Kriging model have been widely used in the surrogate-assisted evolutionary algorithms (SAEAs). Based on their characteristics, a global and local surrogate-assisted differential evolution algorithm (GL-SADE) for high-dimensional expensive problems is proposed in this article, in which a global RBF model is trained with all samples to estimate a global trend, and then its optima is used to significantly accelerate the convergence process. A local Kriging model prefers to select points with good predicted fitness and great uncertainty, which can effectively prevent the search from getting trapped into local optima. When the local Kriging model finds the best solution so far, a reward search strategy is executed to further exploit the local Kriging model. The experiments on a set of benchmark functions with dimensions varying from 30 to 200 are conducted to evaluate the performance of the proposed algorithm. The experimental results of the proposed algorithm are compared to four state-of-the-art algorithms to show its effectiveness and efficiency in solving high-dimensional expensive problems. Besides, GL-SADE is applied to an airfoil optimization problem to show its effectiveness.
径向基函数（RBF）模型和克里金模型已广泛应用于代理辅助进化算法（SAEAs）中。基于它们的特点，本文提出了一种用于高维代价高昂问题的全局和局部代理辅助差分进化算法（GL-SADE），其中全局 RBF 模型使用所有样本进行训练，以估计全局趋势，然后使用其最优值来显著加速收敛过程。局部克里金模型更倾向于选择具有良好预测适应性和较大不确定性的点，这可以有效地防止搜索陷入局部最优。当局部克里金模型找到迄今为止的最佳解决方案时，执行奖励搜索策略以进一步利用局部克里金模型。通过对维度从 30 到 200 的一组基准函数进行实验，评估了所提出算法的性能。将所提出算法的实验结果与四种最先进的算法进行比较，以证明其在解决高维代价高昂问题方面的有效性和效率。此外，GL-SADE 还应用于翼型优化问题，以展示其有效性。
